Published Online September 2019 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijieeb.2019.05.03
Evaluation of Different Machine Learning Methods for Caesarean Data Classification
Department of Material Science and Engineering, Kastamonu University, Kastamonu, Turkey 1
Department of Computer Engineering, Kastamonu University, Kastamonu, Turkey 2
Email: osama.s.alsharif@gmail.com, albauodi@gmail.com, abcydrawi@gmail.com, kemalakyol48@gmail.com
Received: 17 July 2019; Accepted: 05 August 2019; Published: 08 September 2019
Abstract—Recently, a new dataset has been introduced used when the expectation objective is a discrete regard about the caesarean data. In this paper, the caesarean data or a class stamp. At the point when the expectation was classified with five different algorithms; Support objectives continuous, regression is the suitable technique Vector Machine, K Nearest Neighbours, Naïve Bayes, to utilize. There is a diverse application for Machine Decision Tree Classifier, and Random Forest Classifier. Learning, the most critical of which is data mining. The dataset is retrieved from California University Individuals are regularly inclined to committing errors website. The main objective of this study is to compare amid investigations. This makes it troublesome for them selected algorithms’ performances. This study has shown to discover answers for specific issues each instance in that the best accuracy that was for Naïve Bayes while the any data-set utilized by machine learning algorithms is highest sensitivity which was for Support Vector spoken to utilizing a similar arrangement of features. The Machine. features might be continuous, categorical or binary. On the off chance that occasions are given with known marks Index Terms—Caesarean data, machine learning, (the identical correct outputs) the learning is called Decision Tree, K-Nearest- Neighbours, Naïve Bayes, supervised, rather than unsupervised realizing, where Support Vector Machine, Random Forest Classifier. examples are untagged. By applying these unsupervised
calculations, specialists would like to find obscure, but
advantageous, classes of items [2].
The main objective of this study is to evaluate several I. INTRODUCTION
machine learning algorithms classify the caesarean
The point of this examine is to investigate the section. execution various techniques on caesarean dataset which The rest of this study is organized as follows; Section 2 is a main issue in bioinformatics examination of medical introduces the dataset, and methodology used in this sciences. In this paper the proposed study was performed study. Section 3 addresses the experiments and results using 'scikit-learn' as the back-end learning library in the carried out on the dataset. Finally, Section 4 draws the Python 2.7 programming language on the Anaconda conclusion. system platform and generally various experiment are well-done that incorporate gathering of generous scale data. These test systems are said to be basic with a view II. DATASET to accomplish a complete assurance. Be that as it may,
The dataset is retrieved from the \"University of then again, such many tests could entangle the primary
California, Irvine Machine Learning Repository\". It determination process and lead to the trouble in getting
contains about 80 pregnant women with the most the outcome. This kind of inconvenience could be settled
important characteristics of delivery problems in the with the guide of machine acknowledging which could be
medical field. The dataset contains 80 instances and 5 used explicitly to get the last item with help of a few
attributes; the Table 1 below illustrates the properties of computerized reasoning techniques [1]. Artificial
the data (6 attributes). (5 represents the Intelligence is the upgrade of the computer activities to
implement endeavours that normally demand the human
Table 1. Caesarean data attributes intercession, for example, decision making. Settling on
the correct choice for an explicit issue is the fundamental Attribute no Attribute 1 Agefactor for accomplishing our objectives. Therefore,
2 Delivery numbernumerous machine learning strategies are used for both
3 Delivery timeclassification and regression issues. Classification is 4 Blood of Pressure 5 Heart Problem 6 Caesarean 0 or 1
Copyright © 2019 MECS I.J. Information Engineering and Electronic Business, 2019, 5, 19-23
O.S.S. Alsharif,K.M. Elbayoudi1, A.A.S. Aldrawi1, K. Akyol2
1
20 Evaluation of Different Machine Learning Methods for Caesarean Data Classification
inputs and 1 represents the output). In the output, 0 means \"decision no caesarean on\" and 1 means \"decision caesarean time\".
III. METHODOLOGY
3.1. Machine learning
Its aim is to arrange the information and get results that can be useful for different fields in our life. Through the experience, we will explore data by the program that can be the help to get good decisions. For instance, Google Maps exam for speed traffic through unknown area information from cell phones. This enables Google to lessen travel time by suggesting the fastest routes. The input data in Google outline, Maps accomplices, Road see Satellites, Area administrations, and Google Maps producers [3].
Classification is an important process in machine learning as well as in data mining. In addition, it used to compose our classifier set of training with class labels. In our study, we have only two output result: + (the positive class) or − (the negative class).
The machine learning algorithms used in this study were introduced below briefly.
Support Vector Machines (SVM): SVMs spin around “margin\" thought both sides of hyperplane which isolates two classes of data. The expansion of the margin and the greatest possible separation between the isolating hyper-plane and the instances for either side of it showed that the uppermost bound to the regular speculation error was diminished [4].
K-Nearest-Neighbours (k-NN): K-NN means a case of occurrence-based inclining and usually utilized for order where the assignment is to arrange the inconspicuous precedents dependent on the databases. Dimensional space using to display the comprehension, where the digits of attributes or properties of the observation are present. From another point of view, it is described by its similarity to any data centred in the structure. K-NN selects a new point category through selecting the nearest K indicates the new instance and selects the most common class by means of a plurality vote to be the new point class [5-6]. Naïve Bayes (NB): Naïve Bayes induction algorithms were earlier explained to be amazingly accurate on many classification duties even when the limited autonomy assumption on which they are based is disrupted. Naïve Bayes is a perfect performance to the zero-one model used in classification [7]. The incorrect predictions had been defined as the error production in the Naïve Bayes. In contrast to many other loss tasks, like the squared mistake, this does not penalize the incorrect probability evaluation as long because as the best possibility is allocated to the right class [8].
Decision Tree (DT): DT supports the most widely used statistical and ML classifiers. It is a dynamic construction executes and overcomes the partition approach. It is a
non-parametric classification and regression technique. It can be easily represented as if-then rules. Its realistic description makes the issue clear to the follower and ready to decipher the outcome and clear [9, 10].
Random Forest (RF): Successive trees increase the load to key points incorrectly expected by previous forecasters. The forecast for the weighted vote, in the end, will be taken. They do not rely on previous trees; where each is freely created by using a dataset \"Bootstrap test\". Each node in each random forest makes the typical use of a subset of indicators haphazardly selected at that node. This rather counterintuitive strategy is better than number of other classifiers, including discriminant examination, support vector machines and neural frameworks [11]. 3.2. Confusion matrix
It is an error matrix related to the problem of statistical classification in machine learning. Confusion Matrix table, Table 2, related with the descript the classifier in by testing the data which the true values are known. It allows the imagination of the performance of an algorithm. Confusion’s confirmation allows between classes e.g.one class is ordinarily mislabelled as the other. Parameters of confusion matrix are given in Table 3.
Table 2. Confusion matrix. Predicted values Yes No Actual Values Yes TP FP No FN TN Table 3. Parameters of confusion matrix
Abbreviations TP ExplanationThe number of pregnant women that the program predicts to have caesarean among the pregnant women had a caesarean by gynecologist. The total of pregnant women which the classifier algorithm predicts to do not the time to caesarean among the pregnant women had not caesarean by gynecologist. The total of pregnant women which predicted classifier algorithm that it is the time of caesarean, while the gynecologist decided to reverse that. The number of pregnant women that predicted by the program that it's not the time of caesarean, while the gynecologist decided to reverse that. TN FP FN Most of the performance measures figured by confusion matrix. Where, True Positive (TP), True Negative (TN), False Positive (FP) and False Negative (FN) represents the precision parameters (as illustrated in the Table 2). There are diverse ways for measures to getting results for sensitivity, accuracy one of that measurements are the following equations:
Copyright © 2009 MECS I.J. Information Engineering and Electronic Business, 2009, 1, 25-32
Evaluation of Different Machine Learning Methods for Caesarean Data Classification 21
SensitivityTP/(TPFN) (1)
Accuracy =TP + TN/TP + FP + TN + FN (2)
IV. EXPERIMENTS AND RESULTS
Flowchart of the proposed study is introduced in Figure 1.
Table 4 contains the results of the Support Vector Machine classification algorithm. Based on Table 4, through 5 data point that the gynaecologist considered as actual, four were positive and one was negative, the Support Vector Machine found. In addition, out of 19 data considered passive by gynaecologist, the Support Vector Machine found that 10 were passive and 9 were actual. The Support Vector Machine, therefore, gave values of accuracy of 58.33%, sensitivity of 90.90%.
Table 5. The results of the K-Nearest-Neighbors TP=2 FP=5 7
FN=3 TN=14 17 5 19 24 Table 5 contains the results of the k-NN classification algorithm. Based on Table 5, through 5 data point that the gynaecologist considered as actual. Two were positive and Three was negative, the k-NN found. In addition, out of 19 data considered passive by gynaecologist, the Support Vector Machine found that 14 were passive and 5 were actual. The k-NN, therefore, gave values of accuracy of 66.66%, sensitivity of 82.35%.
Table 6. The results of the Naïve Bayes TP=3 FP=5 8
FN=2 TN=14 16 5 19 24
Fig.1. A flowchart of the proposed study.
Before obtaining our results, the dataset split into two
sections; the training dataset is 70% and the test is 30%.
The train data sent to as input data machine learning algorithms to classification of caesarean data. After, successes of the models were evaluated on test data. The performance evaluations were presented in confusion matrix structure. Five classifiers algorithms are tested. The outcomes are demonstrated in Tables 4-8.
We executed the programming code several times, but we got slightly different results for each executing operation. Sometimes the Naïve Bayes algorithm had the best accuracy, but in other operation execution, the Support Vector Machine algorithm got the best accuracy. The values of precisions are shown in the following tables (Table 4 to Table 8).
Table 4. The results of the Support Vector Machine TP = 4 FP = 9 13 FN = 1 TN = 10 11 5 19 24Table 6 contains the results of the Naïve Bayes classification algorithm. Based on Table 6, through 5 data point that the gynaecologist considered as actual. Three were positive and two was negative, the Naïve Bayes found. In addition, out of 19 data considered passive by gynaecologist, the Naïve Bayes found that 14 were passive and 5 were actual. The Naïve Bayes, therefore, gave values of accuracy of 70.83%, sensitivity of 87.50%.
Table 7. The results of the Naïve Bayes TP=3 FP=9 12
FN=2 TN=10 12 5 19 24 Table 7 contains the results of the Decision Tree
Classifier classification algorithm. Based on Table 7, through 5 data point that the gynaecologist considered as actual. Three were positive and two was negative, the Decision Tree Classifier found. In addition, out of 19 data considered passive by gynaecologist, the Decision Tree Classifier found that 10 were passive and 9 were actual. The Decision Tree Classifier, therefore, gave values of accuracy of 54.16%, sensitivity of 83.33%.
Table 8. The results of the Random Forest Classifier TP = 3 FP = 9 12 FN = 2 TN = 10 12 5 19 24 Copyright © 2019 MECS I.J. Information Engineering and Electronic Business, 2019, 5, 19-23
22 Evaluation of Different Machine Learning Methods for Caesarean Data Classification
Table 8 contains the results of the Random Forest Classifier classification algorithm. Based on Table 8, through 5 data point that the gynaecologist considered as actual. Three were positive and two was negative, the Random Forest Classifier found. In addition, out of 19 data considered passive by gynaecologist, the Random Forest Classifier found that 10 were passive and 9 were actual. The Random Forest Classifier, therefore, gave values of accuracy of 54.16%, sensitivity of 83.33%.
Lastly, all results are combined and illustrated in Table 9, and their graphic exhibit is shown in Figure 2.
Table 9. Results of the used methods. Best outcomes highlighted in
bold. Algorithms SVM k-NN NB DT RF )%( Accuracy 58.33 66.66 70.83 54.16 54.16 )%( Sensitivity 90.90 82.35 87.50 83.33 83.33
Fig.2. Graphical results of the classifications
V. CONCLUSIONS
This paper has shown the results of accuracy for different five classification algorithms. The study carried out to explore which the best algorithm between that technique of classifier algorithms, Support Vector Machine, K-Nearest-Neighbors, Naïve Bayes, Decision Tree Classifier, and Random Forest Classifier. As we know; the nature of the dataset usually affecting the performance of any learning algorithm. We have compered the performances of different classifier algorithms. We got the best accuracy that was for Naïve Bayes while the highest sensitivity which was for Support Vector Machine.
ACKNOWLEDGMENT
The authors thank the UCI Machine Learning Repository for providing publically available caesarean dataset.
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Abdusalam Ahmed Salem Aldrawi, He graduates and obtained the B.Sc. Degree in Data Analysis and computer science in 2012 at EL-Merghep University faculty of Economics and Commerce, and now student in master degree program in Kastamonu university institute of sciences Materials science and Engineering
Department.
Kemal Akyol, He received his B.Sc. in Computer Science Department from Gazi University, Ankara/Turkey in 2002. He received his M.Sc. degree from Natural and Applied Sciences, Karabuk University, Karabuk, Turkey and Ph.D. degree from the same department. His research interests include data mining, decision support
systems and expert systems.
Authors’ Profiles
Osama Alsharif, He received his B.Sc. in Information Technology Department from higher institute of instructors from Benghazi in Fall 2007/2008. His graduation project was The relationship between online computing systems and users. He is a master’s student at the department of computer engineering at Kastamonu
University.
Khaled Elbayoudi, He received his B.Sc. in Internet System Department from
Faculty of Information Technology from
Misurata University in Fall 2010/2011. His
graduation project was Online examination.
He is a master’s student at the department
of computer engineering at Kastamonu
University.
How to cite this paper: O.S.S. Alsharif, K.M. Elbayoudi, A.A.S. Aldrawi, K. Akyol, \"Evaluation of Different Machine Learning Methods for Caesarean Data Classification\Electronic Business(IJIEEB), Vol.11, No.5, pp. 19-23, 2019. DOI: 10.5815/ijieeb.2019.05.03
Copyright © 2019 MECS I.J. Information Engineering and Electronic Business, 2019, 5, 19-23
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